Cell biology of micro-organisms and the evolution of the eukaryotic cell
Bibliographic record
Abstract
A comparative or evolutionary approach is a powerful addition to the cell biologist's armory.It can provide context for observations in more classical model systems; it can elucidate the forces shaping the morphology, organization, and complexity of the cell; and it can identify new phenomena that may eventually be recognized as crucial to how cells work.Over the past 15 years, genome sequencing has facilitated comparative work in microbial eukaryotes, while advances in cellular imaging technologies have opened up prokaryotes as models for the study of cell biology.At the 2011 ASCB meeting, the Minisymposium entitled "Cell Biology of Micro-organisms and the Evolution of the Eukaryotic Cell" highlighted mechanisms that underpin the evolution of complexity in cells, described new and unexpected microbial cellular phenomena, and reported the development of technologies that will allow us to explore new avenues in the study of microbial cells.The session began with the theme of eukaryotic cell evolution and emergent complexity.Using a combination of comparative genomics and structural modeling Fred Mast (University of Alberta) described an evolutionary model for how multiple organellar cargoes compete for transport by myosin V (Mast et al., 2011).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".